Commutes are a large part of everyday life that can be influenced by many factors. Using commute data from the Urban Institute, as well as US Census data, we examined how race and education influenced spatial mismatch in two unique cities, Seattle and Baltimore. We used Principal Component Analysis to test our selected variables in both cities, and found that in Seattle, there was a significant relationship between certain race and education variables and spatial mismatch, while in Baltimore we were unable to find clear results.
Prior to the COVID pandemic, commuting to work was an almost ubiquitous experience that those in the workforce dealt with. Whether by car, bus, train, bike, or foot, people commute to work. But not all commutes are created equal. For some, commuting is a simple and quick process, while for others, access to their jobs requires more investment, often in time. To better understand how commutes vary across different regions, specifically for those who are looking for low income jobs, we used data from The Urban Institute and the American Community Survey to understand if racial demographics and education levels impact the distance workers must travel to arrive at low income jobs.
We focused on two metropolitan areas, Seattle and Baltimore, because each city has unique attributes that play a role in the makeup of the populations. Seattle is a rapidly increasing city (Balk, 2023) while Baltimore suffers from urban flight (McFadden, 2018). In Seattle, high income residents tend to live closer to the urban core, while decades of divestment in Baltimore has led to higher-income residents escaping to the suburbs (The Unequal Commute, 2020). Both cities have long histories of racial segregation, whether de jure or de facto, through neighborhood covenants or redlining. We hoped, through our research, to see if the various differences between Seattle and Baltimore would reveal anything about residents’ commutes. In both cities, job density increased between 2004 and 2015, which could play a role in access to jobs, along with the average commute times, income levels, and location of jobs in the city (Shearer et. al, 2022).
We started our project with commute data collected and published by the Urban Institute (Stern et al., 2020). This data and the subsequent report focuses on four Metropolitan Statistical Areas (MSAs): Seattle, WA, Lansing, MI, Nashville, TN, and Baltimore, MD. These cities are used as case studies to examine transportation access to jobs, specifically among low income workers. They used transportation data from OpenTripPlanner, which creates a model based on transit and road grid data, to calculate travel time between two points, as well as road data from OpenStreetMap, transit data from the Transitland feed registry, and traffic data from INRIX’s 2019 Global Traffic Scorecard. Additionally, they used demographic data from the 2014-18 ACS five year estimates, census defined block group boundaries, and population weighted centroids for each block group from the Missouri Census Data Center. To calculate access to jobs, they included data on opportunities like jobs, hospitals, libraries, and higher education. They include all data collected for each city.
The unequal commute researchers have a nine-step process that they use to calculate the different variables they create. First, they defined the geographic scope, including about a forty-five mile radius to adequately capture workers that could access jobs in the MSAs that they previously defined. Second, they gathered transit and road data as previously discussed to build a representation of the transportation network. Third, they identified start and end points for their routing. Fourth, they calculated travel times, filtering for the fastest itinerary for each route, restricting walk distance within the commute, factoring in possible transfers, and controlling for arrival time. This analysis was set to model travel prior to any effects of the COVID-19 pandemic restrictions. Fifth, they created alternatives for missing routes (like for those who commute from islands into the city of Seattle). Sixth, they adjusted for higher traffic levels during peak commute times. Next, they calculated access to jobs. They summarized the jobs accessible both by public transit and car to low-wage workers by block group, divided by the competitiveness of these jobs or the number of workers who live within a reasonable commuting time of each job. These two equations (by car and transit) were then combined to create an overall job accessibility score for low wage workers by block group. Next, they specifically distinguish between peak and third-shift (late-night) transit accessibility. Finally, they calculated racial disparity in access to jobs by comparing the racial and ethnic distribution of neighborhoods with high levels of spatial mismatch to the racial and ethnic distribution of the whole urban area.
While the Urban Institute’s final dataset included multiple new variables they calculated from the data they gathered, we were particularly interested in one of their variables – spatial mismatch. Spatial mismatch is a normalized measure of the distance between low wage job seekers and access to desirable jobs. Spatial mismatch relies on two factors: how desirable a job is, based on how many people apply for it, and how long it takes to get to the job. Someone who has a high spatial mismatch has a harder time accessing desirable low income jobs, while someone with a low spatial mismatch has easy access to desirable low income jobs. We visualized spatial mismatch in both Seattle and Baltimore, which you can see in Figures 1 and 2.
Figure 1: Spatial Mismatch in the Seattle Area, with 45 mile range